Corpora for Topic Detection and Tracking

Cieri, Christopher; Strassel, Stephanie; Graff, David; Martey, Nii; Rennert, Kara; Liberman, Mark

doi:10.1007/978-1-4615-0933-2_3

Cited by 27 publications

(22 citation statements)

References 0 publications

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“…the two-expert settings used in (Chen, 2006;Lu et al, 2007) or even in the highly professionally organised TDT evaluation on topics in news (Cieri et al, 2002). Zeng et al (2004) used three experts, but they investigated sub-topics of general Web query, which are easier to judge than scientific sub-topics.…”

Section: Cluster Qualitymentioning

confidence: 99%

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Berendt

Krause

Kolbe-Nusser

2010

Information Processing & Management

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Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.

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Section: Cluster Qualitymentioning

confidence: 99%

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Berendt

Krause

Kolbe-Nusser

2010

Information Processing & Management

View full text Add to dashboard Cite

show abstract

“…The documents contain 11,334 distinct words; average document length is 460. The second dataset contains 200 documents from the TDT-1 corpus [24]. TDT documents are slightly longer, average length is 540 words, but the number of distinct words is somewhat smaller: 9,379.…”

Section: Y Imentioning

confidence: 99%

“…Because it is so small, the Hong-Kong parallel corpus has a significant word coverage problem. In order to alleviate the problem, we augmented the corpus with the TDT2 and TDT3 [24] pseudo-parallel datasets. These corpora contain 46,692 Chinese news stories along with their SYSTRAN translations into English.…”

Section: Chinese Resourcesmentioning

confidence: 99%

“…The experiments in this section were performed on three different datasets: TDT2, TDT3 and TDT4 [24]. Most of the training was done on a 4-month subset of the TDT2 dataset.…”

Section: Datasets and Topicsmentioning

confidence: 99%

“…A total of 6363 story pairs were drawn from the dataset (according to the official TDT2 sampling). 1469 of these were manually [24] judged to be on-target (discussing the same topic), and 4894 were judged off-target (discussing different topics). During evaluation the Link Detection System emits a YES or NO decision for each story pair.…”

Section: Evaluation Paradigmmentioning

confidence: 99%

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